Exploratory Data Analysis

library(tidyverse)
library(lubridate)
library(forecast)
library(ggrepel)
library(rvest)
library(plotly)

dja <- read_csv("data/DJA.csv",skip = 4)
gold <- read_csv("data/GOLD_1791-2018.csv",skip = 3)
interest_rate <- read_csv("data/INTERESTRATE_1857-2018.csv",skip = 1)
sap <- read_csv("data/SAP_1871-2018.csv", skip=1)
cpi <- read_csv("data/USCPI_1774-2018.csv", skip=4)
gdp <- read_csv("data/USGDP_1790-2018.csv", skip=2)
wage <- read_csv("data/USWAGE_1774-2018.csv", skip=3)

dja <- dja %>% 
  mutate(Date = parse_date_time(Date,orders = "mdy"))
ggplotly(ggplot(dja,aes(Date, DJIA))+
  geom_line()) %>% 
  layout(legend = list(
      orientation = "h"))

summary(dja)

      Date                          DJIA         
 Min.   :1885-05-02 00:00:00   Min.   :   24.36  
 1st Qu.:1916-02-09 06:00:00   1st Qu.:   70.41  
 Median :1946-10-14 12:00:00   Median :  199.28  
 Mean   :1949-03-11 07:36:39   Mean   : 2354.03  
 3rd Qu.:1982-06-06 06:00:00   3rd Qu.: 1002.07  
 Max.   :2018-09-10 00:00:00   Max.   :26616.71  

dja %>% 
  mutate(dif = (DJIA - lag(DJIA, default = DJIA[1]))/lag(DJIA, default = DJIA[1])) %>% 
ggplot(.,aes(Date, dif))+
  geom_rect(fill="firebrick", 
            xmin=parse_date_time("01-01-1930",orders = "mdy"),
            xmax=parse_date_time("01-01-1940",orders = "mdy"),
            ymin=-1,
            ymax=1,
            alpha=0.5)+
    geom_line()

url <- "https://www.caproasia.com/2016/04/12/economic-crisis-since-1900-2015/"
crisis <- url %>%
  read_html() %>% 
  html_nodes(css = 'table') %>% 
  html_table(header = T)
crisis <- crisis[[1]] %>% 
  filter(Affected %in% c("United States","Global")) %>% 
  separate(Period,c("desde","hasta")," – ")

Expected 2 pieces. Missing pieces filled with `NA` in 10 rows [1, 4, 5, 6, 7, 8, 9, 10, 11, 12].

#en realidad las que terminan en "s" no duran toda la década. Las agrego a mano.
  # mutate(hasta = parse_date_time(case_when(grepl("s",desde)~as.numeric(str_extract(desde,"[[:digit:]]*"))+10,
  #                          TRUE~ as.numeric(hasta)),"y"),
  #        desde = parse_date_time(str_extract(desde,"[[:digit:]]*"),"y"))
crisis <- crisis %>% 
  mutate(hasta = parse_date_time(case_when(desde=="1970s"~"1979",
                           desde=="1980s"~"1982",
                           desde == "1990s"~"1991",
                           TRUE~hasta),"y"),
         desde = parse_date_time(case_when(desde=="1970s"~"1973",
                           desde=="1980s"~"1981",
                           desde=="1990s"~"1990",
                           TRUE~desde),"y"))

crisis_largas <- na.omit(crisis)
crisis_puntuales <- crisis %>% 
  filter(is.na(hasta))
dja <- dja %>% 
  mutate(dif = (DJIA - lag(DJIA, default = DJIA[1]))/lag(DJIA, default = DJIA[1])) 
ggplot()+
  geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-1,
            ymax=1,
            alpha=0.5)+
    geom_line(data = dja,aes(Date, dif))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")

NA

gold %>% 
  ggplot(., aes(Year, `New York Market Price (U.S. dollars per fine ounce)`))+
  geom_line()+
  geom_vline(xintercept = 1971, color = "red")+
  geom_label_repel(data=data_frame(),aes(x=1971,y=1000,label="Fin del patrón oro"),nudge_x = -5,force=10)

graf <- interest_rate %>% 
  gather(type,rate,2:4) %>% 
  ggplot(., aes(Year,rate,color=type))+
  geom_line()+
  guides(color=guide_legend(nrow=2,byrow=TRUE))+
  theme(legend.position = "bottom")
ggplotly(graf) %>%
  layout(legend = list(
      orientation = "h"
    )
  )

sap %>% 
  summary()

      Year      The S&P Index Average for January  Annual Yield   The Accumulated S&P Index Average for January
 Min.   :1871   Min.   :   3.240                  Min.   :1.140   Min.   :     1.0                             
 1st Qu.:1908   1st Qu.:   7.433                  1st Qu.:3.310   1st Qu.:    11.5                             
 Median :1944   Median :  16.430                  Median :4.500   Median :   122.0                             
 Mean   :1944   Mean   : 261.888                  Mean   :4.356   Mean   : 21442.8                             
 3rd Qu.:1981   3rd Qu.: 122.058                  3rd Qu.:5.390   3rd Qu.:  5360.0                             
 Max.   :2018   Max.   :2791.730                  Max.   :8.710   Max.   :298189.7                             
                                                  NA's   :1                                                    

sap %>% 
  gather(type, value,2:4) %>%
  mutate(type= case_when(type=="The S&P Index Average for January"~"The S&P Index\nAverage for January",
                         type=="The Accumulated S&P Index Average for January"~"The Accumulated S&P\nIndex Average for January",
                         TRUE~type)) %>% 
  ggplot(.,aes(Year,value, color=type))+
  geom_line()+
  facet_grid(type~.,scale="free")+
  theme(legend.position = "bottom",
        strip.text.y = element_text(angle = 0))

ts(sap$`Annual Yield`, start=min(sap$Year), frequency = 1) %>% 
  na.omit() %>%
  auto.arima(.)

Series: . 
ARIMA(0,1,2) 

Coefficients:
          ma1      ma2
      -0.0141  -0.3007
s.e.   0.0802   0.0810

sigma^2 estimated as 0.4111:  log likelihood=-141.37
AIC=288.73   AICc=288.9   BIC=297.68

cpi %>% 
  ggplot(aes(Year,`U.S. Consumer Price Index *`))+
  geom_line()

gdp %>% 
  ggplot(aes(Year,`Real GDP per capita (year 2012 dollars)`))+
  geom_line()

gdp <- left_join(gold, gdp, by = "Year") %>% 
  mutate(gdp_in_gold = `Nominal GDP per capita (current dollars)`/`New York Market Price (U.S. dollars per fine ounce)`,
         Year = parse_date_time(Year,"y")) 
  
ggplotly(ggplot(gdp,aes(Year,gdp_in_gold))+
  geom_line())

library(scales) # to access breaks/formatting functions

Attaching package: ‘scales’

The following object is masked from ‘package:purrr’:

    discard

The following object is masked from ‘package:readr’:

    col_factor

ggplot()+
geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-Inf,
            ymax=Inf,
            alpha=0.5)+
  geom_line(data = gdp %>% 
              filter(Year>parse_date_time(1900,"y"))
            ,aes(Year, gdp_in_gold))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")+
  geom_vline(xintercept = parse_date_time(1971,"y"),color = "gold")+ #fin del patron oro
  scale_x_datetime(date_breaks = "15 years",labels = date_format("%Y") )

wage %>% 
  summary()

      Year      Costs of Unskilled Labor (index 1860 = 100) Production Workers Hourly Compensation (nominal dollars)
 Min.   :1774   Min.   :   31.00                            Min.   : 0.020                                          
 1st Qu.:1835   1st Qu.:   79.65                            1st Qu.: 0.060                                          
 Median :1896   Median :  143.50                            Median : 0.150                                          
 Mean   :1896   Mean   : 2499.49                            Mean   : 3.906                                          
 3rd Qu.:1956   3rd Qu.: 1682.75                            3rd Qu.: 2.555                                          
 Max.   :2017   Max.   :19640.40                            Max.   :32.390                                          
                                                            NA's   :16                                              

ggplotly(
wage %>% 
  left_join(cpi,by="Year") %>% 
  na.omit() %>% 
  mutate(salario_horario_real = `Production Workers Hourly Compensation (nominal dollars)`/`U.S. Consumer Price Index *`) %>% 
  ggplot(.,aes(Year,salario_horario_real))+
  geom_line() 
  )

wage %>% 
  filter(Year>=1900) %>% 
  left_join(gold, gdp, by = "Year") %>% 
  mutate(wg_in_gold = `Production Workers Hourly Compensation (nominal dollars)`/`New York Market Price (U.S. dollars per fine ounce)`,
         Year = parse_date_time(Year,"y")) %>% 
  na.omit() %>% 
ggplot(.,aes(Year,wg_in_gold))+
  geom_line()

---
title: "Exploratory Data Analysis"
output: html_notebook
---

## load

```{r}
library(tidyverse)
library(lubridate)
library(forecast)
library(ggrepel)
library(rvest)
library(plotly)
```


```{r, message=FALSE}
dja <- read_csv("data/DJA.csv",skip = 4)
gold <- read_csv("data/GOLD_1791-2018.csv",skip = 3)
interest_rate <- read_csv("data/INTERESTRATE_1857-2018.csv",skip = 1)
sap <- read_csv("data/SAP_1871-2018.csv", skip=1)
cpi <- read_csv("data/USCPI_1774-2018.csv", skip=4)
gdp <- read_csv("data/USGDP_1790-2018.csv", skip=2)
wage <- read_csv("data/USWAGE_1774-2018.csv", skip=3)
```


## Down Jones Avg
el dja es una serie diaria, todas las demás son anuales.

```{r}
dja <- dja %>% 
  mutate(Date = parse_date_time(Date,orders = "mdy"))

ggplotly(ggplot(dja,aes(Date, DJIA))+
  geom_line()) %>% 
  layout(legend = list(
      orientation = "h"))

summary(dja)
```


```{r}

dja %>% 
  mutate(dif = (DJIA - lag(DJIA, default = DJIA[1]))/lag(DJIA, default = DJIA[1])) %>% 
ggplot(.,aes(Date, dif))+
  geom_rect(fill="firebrick", 
            xmin=parse_date_time("01-01-1930",orders = "mdy"),
            xmax=parse_date_time("01-01-1940",orders = "mdy"),
            ymin=-1,
            ymax=1,
            alpha=0.5)+
    geom_line()

```

armo una lista de las crisis conocidas

```{r}
url <- "https://www.caproasia.com/2016/04/12/economic-crisis-since-1900-2015/"
crisis <- url %>%
  read_html() %>% 
  html_nodes(css = 'table') %>% 
  html_table(header = T)

crisis <- crisis[[1]] %>% 
  filter(Affected %in% c("United States","Global")) %>% 
  separate(Period,c("desde","hasta")," – ")


#en realidad las que terminan en "s" no duran toda la década. Las agrego a mano.
  # mutate(hasta = parse_date_time(case_when(grepl("s",desde)~as.numeric(str_extract(desde,"[[:digit:]]*"))+10,
  #                          TRUE~ as.numeric(hasta)),"y"),
  #        desde = parse_date_time(str_extract(desde,"[[:digit:]]*"),"y"))

crisis <- crisis %>% 
  mutate(hasta = parse_date_time(case_when(desde=="1970s"~"1979",
                           desde=="1980s"~"1982",
                           desde == "1990s"~"1991",
                           TRUE~hasta),"y"),
         desde = parse_date_time(case_when(desde=="1970s"~"1973",
                           desde=="1980s"~"1981",
                           desde=="1990s"~"1990",
                           TRUE~desde),"y"))


```




```{r}
crisis_largas <- na.omit(crisis)
crisis_puntuales <- crisis %>% 
  filter(is.na(hasta))


dja <- dja %>% 
  mutate(dif = (DJIA - lag(DJIA, default = DJIA[1]))/lag(DJIA, default = DJIA[1])) 
ggplot()+
  geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-1,
            ymax=1,
            alpha=0.5)+
    geom_line(data = dja,aes(Date, dif))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")
  

```


Este gráfico me da la sensación de que todo estuviera corrido a la derecha (mirando las crisis puntuales vs los picos)

## Gold

```{r}
gold %>% 
  ggplot(., aes(Year, `New York Market Price (U.S. dollars per fine ounce)`))+
  geom_line()+
  geom_vline(xintercept = 1971, color = "red")+
  geom_label_repel(data=data_frame(),aes(x=1971,y=1000,label="Fin del patrón oro"),nudge_x = -5,force=10)
```


## interest_rate


```{r}
graf <- interest_rate %>% 
  gather(type,rate,2:4) %>% 
  ggplot(., aes(Year,rate,color=type))+
  geom_line()+
  guides(color=guide_legend(nrow=2,byrow=TRUE))+
  theme(legend.position = "bottom")
ggplotly(graf) %>%
  layout(legend = list(
      orientation = "h"
    )
  )
```

- La tasa de largo plazo es una serie mucho más suave (eso es un dato conocido de finanzas no?)
- Los surplus funds también parecen ser más volátiles hasta los 40


## sap


```{r}
sap %>% 
  summary()
sap %>% 
  gather(type, value,2:4) %>%
  mutate(type= case_when(type=="The S&P Index Average for January"~"The S&P Index\nAverage for January",
                         type=="The Accumulated S&P Index Average for January"~"The Accumulated S&P\nIndex Average for January",
                         TRUE~type)) %>% 
  ggplot(.,aes(Year,value, color=type))+
  geom_line()+
  facet_grid(type~.,scale="free")+
  theme(legend.position = "bottom",
        strip.text.y = element_text(angle = 0))

```



```{r}
ts(sap$`Annual Yield`, start=min(sap$Year), frequency = 1) %>% 
  na.omit() %>%
  auto.arima(.)

```

## CPI

```{r}
cpi %>% 
  ggplot(aes(Year,`U.S. Consumer Price Index *`))+
  geom_line()
```

## gdp
```{r}
gdp %>% 
  ggplot(aes(Year,`Real GDP per capita (year 2012 dollars)`))+
  geom_line()
```



me interesa ver el PBI normalizado por el crecimiento poblacional, y además normalizado por la cantidad de oro que puede comprar (en lugar de normalizar por el CPI):

```{r}


gdp <- left_join(gold, gdp, by = "Year") %>% 
  mutate(gdp_in_gold = `Nominal GDP per capita (current dollars)`/`New York Market Price (U.S. dollars per fine ounce)`,
         Year = parse_date_time(Year,"y")) 
  
ggplotly(ggplot(gdp,aes(Year,gdp_in_gold))+
  geom_line())
```

A partir del 1900 pareciera que se arman 3 ciclos muy largos

- 1914-1933
- 1933-1980
- 1980-2012


Agregando referencias históricas de las crisis conocidas

```{r}
library(scales) # to access breaks/formatting functions
ggplot()+
geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-Inf,
            ymax=Inf,
            alpha=0.5)+
  geom_line(data = gdp %>% 
              filter(Year>parse_date_time(1900,"y"))
            ,aes(Year, gdp_in_gold))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")+
  geom_vline(xintercept = parse_date_time(1971,"y"),color = "gold")+ #fin del patron oro
  scale_x_datetime(date_breaks = "15 years",labels = date_format("%Y") )
```

La guerra de sesesión de EEUU fué entre el 12 de abril de 1861 y el 9 de abril de 1865 

A partir de ahí el pbi en oro crece hasta el fin del patron oro.

## wage

```{r}
wage %>% 
  summary()
```

Podemos deflactar el salario horario por el CPI

```{r}
ggplotly(
wage %>% 
  left_join(cpi,by="Year") %>% 
  na.omit() %>% 
  mutate(salario_horario_real = `Production Workers Hourly Compensation (nominal dollars)`/`U.S. Consumer Price Index *`) %>% 
  ggplot(.,aes(Year,salario_horario_real))+
  geom_line() 
  )
```


El salario real tiene una tendencia creciente hasta el 78' y allí se estanca hasta el 2013



```{r}
wage %>% 
  filter(Year>=1900) %>% 
  left_join(gold, gdp, by = "Year") %>% 
  mutate(wg_in_gold = `Production Workers Hourly Compensation (nominal dollars)`/`New York Market Price (U.S. dollars per fine ounce)`,
         Year = parse_date_time(Year,"y")) %>% 
  na.omit() %>% 
ggplot(.,aes(Year,wg_in_gold))+
  geom_line()
```

Se ven los mismos tres períodos. Pero a diferencia del GDP, el período 1980-2012 tiene un nivel más bajo que el anterior. 
 

¿ Si quisieramos comparar ingrsos con algún revenue tendríamos usar S&P o DJA?



